IDENTIFYING A SEPSIS SUBPHENOTYPE CHARACTERIZED BY DYSREGULATED LIPOPROTEIN METABOLISM USING A SIMPLIFIED CLINICAL DATA ALGORITHM
Background: Cholesterol metabolism is dysregulated in sepsis contributing to patient heterogeneity. Subphenotypes displaying lower lipoprotein levels and higher mortality (previously subphenotyped hypolipoprotein phenotype [HYPO]) or higher lipoprotein levels and lower mortality (previously subphenotyped normolipoprotein phenotype [NORMO]) were described. We developed a simplified clinical algorithm for bedside subphenotype recognition. Methods: We analyzed data from four prospective studies (internal dataset), focusing on HYPO and NORMO subphenotypes. A 1,000-tree random forest classifier and logistic regression models were built, using clinical features to predict subphenotypes. Performance was evaluated by comparing predictions to actual subphenotypes derived from a machine learning model. The model was applied to an external dataset of 281 patients from three French studies. Results: The internal cohort consisted of 386 patients (median age, 63 years; 46% female). Four clinical features (hepatic SOFA, cardiovascular SOFA, low [low-density lipoprotein cholesterol {LDL-C}] and high-density lipoprotein cholesterol [high-density lipoprotein cholesterol {HDL-C}]) predicted HYPO versus NORMO subphenotypes with an area under the receiver operating characteristic curve of 0.86, a sensitivity of 0.771, and a specificity of 0.779. In the internal dataset, 28-day mortality for HYPO versus NORMO patients was 26% versus 15%, and in the external cohort, 30% versus 10%. HYPO internal versus external dataset LDL-C levels were similar ( P = 0.99), but HDL-C ( P = 0.02) levels were different. Median NORMO internal versus external dataset LDL-C ( P = 0.99) and HDL-C ( P = 0.12) levels were similar. HYPO patients had lower LDL-C, HDL-C and total cholesterol than NORMO patients in both internal and external datasets. Conclusions: Our simplified clinical data algorithm may allow for bedside recognition of septic patients displaying lipid dysregulation subphenotypes. External validation is needed to verify these results.
Authors
- Charlotte Hopson (ORCID: https://orcid.org/0009-0003-5174-0543)
- Andrew Bertrand
- Dongyuan Wu (ORCID: https://orcid.org/0000-0001-9725-3239)
- Kiley Graim (ORCID: https://orcid.org/0000-0002-4569-8444)
- Susmita Datta (ORCID: https://orcid.org/0000-0002-7408-699X)
- Dawoud Sulaiman
- Faheem W. Guirgis (ORCID: https://orcid.org/0000-0002-4445-3939)
- Srinivasa T. Reddy (ORCID: https://orcid.org/0000-0003-2959-9531)
- Sébastien Tanaka (ORCID: https://orcid.org/0000-0003-4698-3287)
- Daniel A. Hofmaenner (ORCID: https://orcid.org/0000-0002-9334-7753)
- Reinaldo Salomão (ORCID: https://orcid.org/0000-0003-1149-4598)
- Guillaume Labilloy
- Lauren Page Black (ORCID: https://orcid.org/0000-0001-6554-3207)
- Beulah Augustin
- Joanne Bethencourt
Institutions
- Université Claude Bernard Lyon 1 (FR)
- Northwestern University (US)
- Inserm (FR)
- University of California, Los Angeles (US)
- University of Reunion Island (RE)
- University of Florida Health (US)
- University of Florida (US)
- Jacksonville University (US)
- Assistance Publique – Hôpitaux de Paris (FR)
- University Hospital of Zurich (CH)
- Peuplements végétaux et bioagresseurs en milieu tropical (RE)
- Hôpital Bichat-Claude-Bernard (FR)
- University of Florida Health Science Center (US)
- Universidade Federal de São Paulo (BR)
- Florida College (US)
Publication Details
- Journal
- Shock
- Published
- 2025-04-23
- DOI
- https://doi.org/10.1097/shk.0000000000002605
- Citations
- 2
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 2.71